code: def forward(self, input_ids, labels=None):
      loss = 0
      gpt2_outputs = self.gpt2(input_ids)
      hidden_states = gpt2_outputs[0].squeeze()
      logits = self.logit(self.linear(hidden_states))
      batch_size, sequence_length = input_ids.shape[:2]
      logits = logits[range(batch_size), sequence_length]
      if labels is not None:
          loss = self.criterion(logits, labels)
      return loss, logits
  
file_path: underthesea/examples/sentiment/train_gpt2.py
imports: import hydra
  import pytorch_lightning
  from omegaconf import DictConfig
  from torch.optim import SGD
  import torch.nn
  from torchmetrics import F1Score
  from transformers import AutoTokenizer, AutoModelWithLMHead
  from pytorch_lightning.loggers import WandbLogger
  from examples.sentiment.data import MultiLabelClassificationDatamodule
  from underthesea.datasets.uit_absa_hotel.uit_absa_hotel import UITABSAHotel
label_desc: Use image node_id 2 for calling the GPT2TextClassification obj's underlying member method code with example usage: obj.forward(input_ids, labels) and returns: loss,  logits